code: def __init__(self, conv1, conv2, hetero, hidden_size):
      super(HeteroGNN, self).__init__()
  
      self.convs1 = HeteroConv(
          conv1
      )  # Wrap the heterogeneous GNN layers
      self.convs2 = HeteroConv(conv2)
      self.loss_fn = torch.nn.BCEWithLogitsLoss()
      self.bns1 = nn.ModuleDict()
      self.bns2 = nn.ModuleDict()
      self.relus1 = nn.ModuleDict()
      self.relus2 = nn.ModuleDict()
      self.post_mps = nn.ModuleDict()
  
      for node_type in hetero.node_types:
          self.bns1[node_type] = torch.nn.BatchNorm1d(hidden_size)
          self.bns2[node_type] = torch.nn.BatchNorm1d(hidden_size)
          self.relus1[node_type] = nn.LeakyReLU()
          self.relus2[node_type] = nn.LeakyReLU()
  
imports: import argparse
  import copy
  import time
  import numpy
  import networkx
  import sklearn.metrics
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from deepsnap.hetero_gnn import HeteroSAGEConv, HeteroConv, forward_op
label_desc: Use image node_id 1 to create a new HeteroGNN object from inherited base classes: torch.nn with example: obj = HeteroGNN(conv1, conv2, hetero, hidden_size)